Enhancing the performance of data-driven models for monthly reservoir evaporation prediction

Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study

The impact of the suspended sediment load (SSL) on environmental health, agricultural operations, and water resources planning, is significant. The deposit of SSL restricts the streamflow region, affecting aquatic life migration and finally causing a river course shift. As a result, data on suspended sediments and their fluctuations are essential for a number of authorities especially for water resources decision makers. SSL prediction is often difficult due to a number of issues such as site-specific data, site-specific models, lack of several substantial components to use in prediction, and complexity its pattern. In the past two decades, many machine learning algorithms have shown huge potential for SSL river prediction. However, these models did not provide very reliable results, which led to the conclusion that the accuracy of SSL prediction should be improved. As a result, in order to solve past concerns, this research proposes a Long Short-Term Memory (LSTM) model for SSL prediction. The proposed model was applied for SSL prediction in Johor River located in Malaysia. The study allocated data for suspended sediment load and river flow for period 2010 to 2020. In the current research, four alternative models-Multi-Layer Perceptron (MLP) neural network, Support Vector Regression (SVR), Random Forest (RF), and Long Short-term Memory (LSTM) were investigated to predict the suspended sediment load. The proposed model attained a high correlation value between predicted and actual SSL (0.97), with a minimum RMSE (148.4 ton/day and a minimum MAE (33.43 ton/day). and can thus be generalized for application in similar rivers around the world.

Optimum reservoir operation of a networking reservoirs system using conditional atom search optimization and a conditional genetic algorithm

This study aimed to apply conditional atom search optimization (CASO) for searching optimum rule curves in a networking reservoirs system with a reservoir simulation model. The networking reservoirs system consisted of 5 reservoirs located in Sakon Nakhon Province, Thailand. The efficiency of the new optimum rule curves was determined by comparison of operating systems between a single reservoir and a networking reservoirs system. The results displayed circumstances of scarcity and excess of water. Where the circumstances of scarcity are frequency and duration. Whilst, excesses of water are average water and the highest water. In addition, the efficiency of searching for optimum rule curves was compared between conditional genetic algorithm (CGA) and CASO techniques. The new optimum rule curves from the networking reservoirs system had an average excess water of 43.828 MCM/year. This average excess water was less than that found for optimum curves from the single system in which the average excess of water was 45.602 MCM/year. CASO was more efficient in converging optimum rule curve solutions faster than CGA by 40.00%. In conclusion, the CASO can be used to search for optimum networking reservoirs rule curve solutions effectively. For the networking reservoirs system derived water from the upstream reservoirs, an analysis was performed of the downstream reservoir. The results showed that the optimum rule curves using CASO operated as a networking reservoirs system provided higher efficiency than a single reservoir system. In addition, they reduced the amount of time that water exceeded the river capacity at a downstream weir by one month compared with the original period of two months.

Prediction of evaporation from dam reservoirs under climate change using soft computing techniques

This study aimed to predict evaporation from dam reservoirs using artificial intelligence considering climate change. Mahabad Dam, near Lake Urmia, in northwestern Iran, is used to investigate the proposed approach. There are three parts to the study presented herein. In the first part, two machine learning models, namely group method of data handling (GMDH) and least squares support vector regression (LS-SVR), were used to model the inflow to the dam reservoir. Temperature, precipitation, and inflow during the previous month from 1990 to 2017 were used as input data. In the second part, the evaporation from the dam reservoir was modeled using the adaptive neuro-fuzzy inference system (ANFIS) and optimized ANFIS using Harris hawks optimization (HHO) and the arithmetic optimization algorithm (AOA) optimization algorithms. The input parameters in this part were temperature, precipitation, inflow to the dam reservoir, along with evaporation from the dam reservoir in the previous month. In the third part, precipitation and temperature were predicted using the fifth report of IPCC based on RCP2.6, RCP4.5, and RCP8.5 scenarios for the period 2020-2040. Out of 28 models presented in the fifth report, EC-ERATH and FIO-ESM had the greatest similarity with observational data of temperature and precipitation, respectively. The results of scatter plots and Taylor's diagram showed the higher performance of LS-SVR (root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency (NSE) of 8.65, 4.69, and 0.96) compared to GMDH (RMSE, MAPE, and NSE of 11.65, 7.81, and 0.93) in modeling the inflow. Moreover, both hybrid modes (AOA-ANFIS and HHO-ANFIS) improved the performance of ANFIS in modeling the evaporation from the dam reservoir. The RMSE, MAPE, and NSE values for ANFIS were 0.56, 0.52, and 0.89, respectively, while these values for the AOA-ANFIS (RMSE, MAPE, and NSE of 0.31, 0.24, and 0.93) and HHO-ANFIS (RMSE, MAPE, and NSE of 0.20, 0.30, and 0.96) were improved remarkably. The impact of climate change reduced the inflow to the dam reservoir by about 0.45, 0.80, and 1.7 MCM in RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. Also, the effect of climate change caused the evaporation from the dam reservoir to increase by about 0.2, 0.9, and 1 MCM in RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. The findings of this study show that the correct management of dam reservoirs needs to consider the potential effects of climate change in the future. Moreover, the hybrid machine learning models used in this study can be used to predict the amount of evaporation in other reservoirs.

Accurate discharge and water level forecasting using ensemble learning with genetic algorithm and singular spectrum analysis-based denoising

Forecasting discharge (Q) and water level (H) are essential factors in hydrological research and flood prediction. In recent years, deep learning has emerged as a viable technique for capturing the non-linear relationship of historical data to generate highly accurate prediction results. Despite the success in various domains, applying deep learning in Q and H prediction is hampered by three critical issues: a shortage of training data, the occurrence of noise in the collected data, and the difficulty in adjusting the model’s hyper-parameters. This work proposes a novel deep learning-based Q–H prediction model that overcomes all the shortcomings encountered by existing approaches. Specifically, to address data scarcity and increase prediction accuracy, we design an ensemble learning architecture that takes advantage of multiple deep learning techniques. Furthermore, we leverage the Singular-Spectrum Analysis (SSA) to remove noise and outliers from the original data. Besides, we exploit the Genetic Algorithm (GA) to propose a novel mechanism that can automatically determine the prediction model’s optimal hyper-parameters. We conducted extensive experiments on two datasets collected from Vietnam’s Red and Dakbla rivers. The results show that our proposed solution outperforms current techniques across a wide range of metrics, including NSE, MSE, MAE, and MAPE. Specifically, by exploiting the ensemble learning technique, we can improve the NSE by at least \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2\%$$\end{document}2%. Moreover, with the aid of the SSA-based data preprocessing technique, the NSE is further enhanced by more than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5\%$$\end{document}5%. Finally, thanks to GA-based optimization, our proposed model increases the NSE by at least \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$6\%$$\end{document}6% and up to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$40\%$$\end{document}40% in the best case.

Predicting monthly evaporation from dam reservoirs using LS-SVR and ANFIS optimized by Harris hawks optimization algorithm

Evaporation is a crucial factor in hydrological studies; its precise measurement has always been challenging due to the costly recording tolls. Therefore, machine learning models that can give reliable predictive results with the least information available have been recommended for evaporation prediction. This study was conducted in the central of Iran using the data related to the Doroudzan dam. Several hydrological and meteorological variables, including inflow and outflow of the reservoir, lake area behind the dam, temperature, overflow from the reservoir, precipitation, and evaporation at the previous month, were considered input data to predict the evaporation at the current month. Monthly data from October 1999 to September 2020 were used during the modeling. First, the single adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector regression (LS-SVR) models were evaluated for predicting the amount of evaporation using different scenarios defined based on the different combinations of input variables. The results showed that LS-SVR with RMSE = 2.77, MAPE = 2.48, and NSE = 0.93 provided a better prediction than ANFIS. Second, the Harris hawks optimization (HHO) algorithm was used to optimize the parameters of ANFIS to check for the possibility of performance improvement. The hybrid ANFIS-HHO model predicted the evaporation with RMSE = 2.35, MAPE = 1.55, and NSE = 0.95, respectively. The Taylor's diagram also demonstrated the superior performance of the hybrid ANFIS-HHO model than the LS-SVR and ANFIS models. The best scenario for all three models included all input variables but the area behind the dam into the models. The methodology proposed in this study is useful for predicting the evaporation from dam reservoirs under the influence of various dam variables.